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INDONESIA
Jurnal Sains dan Teknologi
Published by CV ITTC Indonesia
ISSN : -     EISSN : 28077393     DOI : 10.47233
Core Subject : Science, Education, Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering
Jurnal Sains Dan Teknologi (JSIT), merupakan Jurnal Penelitian dan Kajian Ilmiah yang diterbitkan CV.ITTC - INDONESIA dan dikelola langsung oleh Webinar.Gratis dan Even.Gratis yang terbit 3 (tiga) kali dalam setahun. Penyunting menerima kiriman naskah hasil kajian dan penelitian untuk bidang, Teknik Elektro, Teknik Sipil, Teknik Mesin, ,Teknologi Informasi.
Arjuna Subject : Umum - Umum
Articles 164 Documents
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A Implementasi K-Means Clustering dalam Segmentasi Citra Hewan pada Kucing, Kambing, dan Burung Delvi, Syerlin Aprilia; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3632

Abstract

Image segmentation is one of the most important challenges in digital image processing because it determines the successof separating the main object from the background so that visual information can be further analyzed. The problem ariseswhen the object has complex color, texture, and shape characteristics, as in animal images that often have color patternssimilar to their surroundings, making object boundaries difficult to distinguish clearly. This study aims to apply the KMeans Clustering method in the process of animal image segmentation—specifically for cats, goats, and birds—and toevaluate its effectiveness in identifying and separating the main object from the background. The method used is the KMeans Clustering algorithm, an unsupervised learning technique that groups image pixels based on color similarity in theRGB color space through an iterative process until centroid stability is achieved and clusters representing different imageregions are formed. The results show that the K-Means method can produce good segmentation performance for imageswith uniform lighting and simple backgrounds but experiences a decrease in accuracy when the object’s color is similar toits environment. Overall, this algorithm is effective, simple, and can serve as a foundation for developing automatedanimal image identification and classification systems
Penerapan K-Means Clustering untuk Klasifikasi Citra Aksesoris Ekstraksi Warna dan Tekstur GLCM Zubaidah, Rima Puti; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3633

Abstract

The main problem in accessory image recognition lies in the similarity of physical shapes among objects such as bracelets, necklaces, and earrings, which often causes difficulties in the automatic classification process. This study aims to develop an accessory image classification system capable of accurately grouping objects based on a combination of color and texture features using the K-Means Clustering algorithm. The method used includes several preprocessing stages such as resizing images to ensure uniform dimensions and normalizing pixel values to achieve consistent data scales. Color features were extracted using RGB and HSV histograms to represent color variations, while texture features were obtained through the Gray Level Co-occurrence Matrix (GLCM) method with four parameters: contrast, correlation, energy, and homogeneity. All extracted features were then combined and analyzed using the K-Means algorithm with k=3, corresponding to the number of accessory categories. The results show that combining color and texture features produces a more optimal cluster separation compared to using single-feature extraction. The K-Means algorithm successfully grouped accessory images according to their respective categories with high consistency. These findings have potential applications in digital catalog management systems and product recommendation systems on e-commerce platforms.
Penerapan K-Means Clustering Pada Pengolahan Citra Jam Digital, Analog dan Monograph dengan Matlab Dinantia, Triend; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Manual grouping of clock types is time-consuming and prone to errors, necessitating an automatic method to accuratelyclassify digital, analog, and chronograph clocks. This study aims to implement the K-Means Clustering method ingrouping clock types using image processing techniques with Matlab. The applied method involves image processing withcolor space conversion from RGB to LAB, texture feature extraction using Gray-Level Co-occurrence Matrix (GLCM),and grouping using K-Means Clustering algorithm. Analysis was performed by calculating silhouette coefficient andDavies-Bouldin Index to evaluate cluster quality. Results show three clusters formed: analog clocks, digital clocks, andchronograph clocks with 99% accuracy, where 30 out of 30 image data were correctly identified. K-Means Clusteringmethod is proven effective and accurate in determining clock categories.
Analisis Persepsi Pengguna Jalan terhadap Penerapan Sistem Satu Arah: Studi Kasus Jalan Nani Wartabone, Gorontalo Ahmad, Noor Fatmawanti; Abas, Mohamad Ilyas
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3640

Abstract

Traffic congestion in educational areas is a persistent issue in medium-sized Indonesian cities, including Gorontalo. JalanNani Wartabone, adjacent to Gorontalo State University, frequently experiences congestion due to student mobility,vehicle flow, pedestrians, and street vendors. To mitigate this, the city government introduced a one-way traffic system, yetits effectiveness from users’ perspectives has not been fully evaluated. This study examines road user perceptions acrossfive dimensions: comfort, safety, efficiency, accessibility, and satisfaction. Data were collected from 200 respondents(students, pedestrians, drivers, vendors) using questionnaires and analyzed with SPSS through descriptive statistics, crosstabulations, and correlation analysis, complemented by spatial heatmaps from geotagged feedback. Results revealsignificant group differences: students and pedestrians perceived positive effects in traffic order and walkability, whiledrivers and vendors reported reduced accessibility and longer travel times. The study contributes a user-centered,evidence-based framework for inclusive traffic policy in secondary cities

Page 17 of 17 | Total Record : 164

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